Combining Information from Multiple Surveys to Enhance Estimation of Measures of Health
Trivellore E. Raghunathan, University of Michigan
*Nathaniel Schenker, National Center for Health Statistics
Keywords: Complementary surveys, coverage error, measurement error, missing data, non-sampling error, race bridging, self-reported data, small-area estimation.
Survey estimates are often affected by non-sampling errors due to missing data, coverage error, and measurement or response error. Such non-sampling errors can be difficult to assess, and possibly correct for, using information from a single survey. Thus, combining information from multiple surveys can be beneficial. This presentation describes examples of projects undertaken by researchers within and outside the National Center for Health Statistics of the Centers for Disease Control and Prevention, in which information from multiple surveys was combined to adjust for different types of non-sampling errors and thereby enhance estimation of various measures of health. The presentation highlights the goals, techniques, and results of the different projects and discusses issues that can arise when information is combined from multiple surveys.